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Related Experiment Video

Updated: Jan 15, 2026

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
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Model-Free Transformer Framework for 6-DoF Pose Estimation of Textureless Tableware Objects.

Jungwoo Lee1, Hyogon Kim1, Ji-Wook Kwon1

  • 1Smart Mobility Research Center, Korea Institute of Robotics and Technology Convergence (KIRO), Pohang 37666, Republic of Korea.

Sensors (Basel, Switzerland)
|October 16, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel geometry-based method for estimating the six-degree-of-freedom (6-DoF) pose of textureless tableware. The transformer-based approach enables robots to accurately grasp and collect items in restaurant settings.

Keywords:
6-DoF pose estimationgeometry-based featuressurface normal vectortableware manipulationtransformer encoder architecture

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Area of Science:

  • Robotics
  • Computer Vision
  • Artificial Intelligence

Background:

  • Estimating the six-degree-of-freedom (6-DoF) pose of tableware is crucial for robotic manipulation in restaurants.
  • Conventional methods struggle with textureless, uniform objects like plates and bowls, hindering autonomous grasping.
  • Existing approaches often rely on texture cues or specific 3D models, which are not always applicable.

Purpose of the Study:

  • To develop a model-free and texture-free 6-DoF pose estimation framework for robotic tableware manipulation.
  • To overcome the limitations of traditional pose estimation methods in restaurant environments.
  • To enable reliable autonomous grasping and collection of tableware by service robots.

Main Methods:

  • A transformer encoder architecture is utilized for pose estimation, processing geometry-based features from depth images.
  • Features include surface vertices and rim normals, providing strong structural priors without texture.
  • The pipeline integrates object detection/segmentation with a pretrained video foundation model and transformer-based pose prediction.

Main Results:

  • The proposed method achieved an average rotational error of 3.53 degrees and a translational error of 13.56 mm across ten tableware types.
  • Real-world deployment on a mobile robot platform demonstrated successful autonomous recognition and collection of tableware.
  • The geometry-driven approach proved practical for service robotics applications.

Conclusions:

  • The novel geometry-based framework effectively addresses the challenge of 6-DoF pose estimation for textureless tableware.
  • This approach enhances the capabilities of service robots in restaurant environments.
  • The model-free and texture-free method offers a practical solution for autonomous robotic manipulation.